3 years ago

Inferring Multidimensional Rates of Aging from Cross-Sectional Data.

Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec, Nicholas Eriksson, Percy Liang

Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual only observed once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual as a nonlinear function of a latent, linear, and low dimensional aging time-series. We prove that when this nonlinear function is constrained to be order-isomorphic, the model family is identifiable solely from cross-sectional data if the distribution of time-independent variation is known. Applied to the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging that are associated with diseases, mortality, and aging risk factors.

Publisher URL: http://arxiv.org/abs/1807.04709

DOI: arXiv:1807.04709v2

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